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Debunking Nagging Myths About AI and Customer Experience

Commonly held beliefs about AI are usually more science fiction than reality--at least for the time being.

For many people, hearing the term "artificial intelligence" generates a wide range of reactions, ranging from enthusiasm to hysteria about machines taking over the world. Actually, the latter has been an unwarranted worry for most of the last century.

Imagination runs wild as to how AI actually will affect our future, and it's interesting to dream about what it means by watching AI advancements in research centers.

But commonly held beliefs in AI are usually more science fiction than reality--at least for the time being. Instead of falling into AI paralysis, people should assess what's real and what's not when it comes to AI and how it translates to delivering better customer experiences (also known as CX).

Here are a list of common myths, provided to eWEEK as industry information from Vince Jeffs, Director of Strategy & Product Marketing at Pegasystems, which has been developing business process and customer relationship management software for enterprises since 1983.

As it relates to CX, technology can sift through massive data sources and find patterns we'll never see. Unlike the human mind, computers never get tired with mundane, repetitive tasks. In the right applications, they not only drive increased revenue but also provide unexpected value to the customer.

That said, none of this replaces the uniqueness of human thinking. Instead, these technologies augment our human intelligence and make our work and experiences more productive.

Myth No. 2: AI Will Completely Replace the Workforce

For many workers, AI is a looming iceberg that could destroy their livelihood. However, successful companies rarely destroy jobs. Instead, like the laws of matter, they morph into new forms.

For decades, businesses have improved efficiency by streamlining redundant tasks to free employees to do more meaningful work, as opposed to replacing their jobs entirely. In many cases, AI technology works hand in hand with people to enable them to do their jobs better, smarter, and faster to provide better CX.

AI is becoming increasingly critical to business operations, and as a result, organizations need to employ data experts who understand the technology. That said, they don't need dozens of PhDs to accomplish AI goals.

A better solution would be to hire and assemble a small team with the right mix of talent--those who understand machine learning technologies, applied statistics, the business, content management, and project management. The team should have clear objectives and milestones, with progress measured against these goals and incentives for workers to turn ideas into profits.

Myth No. 4: Don't Delete Any Data, Because AI Might Need It

Data can be comparable to the clothes you bought 30 years ago that you're still holding onto in the attic: It just ends up occupying space and, more often than not, it ends up serving as a dust magnet.

Organizations need to use common sense, business intuition, and convenience when deciding which data might be useful in the future. Survey and demographic modeling data go stale over time, but key digital, behavioral interactions can serve a purpose for a longer period of time.

First-party purchase data can prove useful for up to three years and even longer for infrequent purchases, and it should be summarized for quarterly and annual trend spotting and forecasting.

AI covers a broad range of models and techniques--from regression models and decision trees to emerging techniques, such as deep learning.

But for organizations that aren't applying any of these methods, they can start with simple and low-risk approaches to using AI for better CX, like using technologies that employ Bayesian algorithms to identify the next best action based on response data, or finding compatible products to offer based on collaborative filtering.

Developing this level of familiarity with the technology can help businesses feel comfortable using models that calculate customer lifetime value and churn propensity.